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Clawhub Convex

OrganizationPopular
openclaw
clawhub-convex

Apply ClawHub-specific Convex conventions and route to the right managed Convex skill. Use for any change under convex/, Convex commands or deployment targeting, query performance, migrations, retention, runtime validation, or skill stat reads and writes in the ClawHub repository.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameclawhub-convex
Stars
9.5K
Forks
1.5K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Clawhub Convex AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/clawhub/tree/main/.agents/skills/clawhub-convex
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openclaw/clawhub.git /tmp/clawhub
mkdir -p .claude/skills
cp -r /tmp/clawhub/.agents/skills/clawhub-convex .claude/skills/clawhub-convex
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clawhub Convex in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Clawhub Convex on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Clawhub Convex is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ClawHub Convex

Use the managed Convex guidance for general framework behavior and this skill for ClawHub's repository-specific boundaries.

Start Here

  1. Read convex/_generated/ai/guidelines.md before editing Convex code.
  2. Name the target runtime before running a Convex command: local, dev, or prod. Include the exact deployment when known and whether the current function/schema code has already been pushed there.
  3. Route to the most specific companion skill:
    • Query cost, indexes, read amplification, subscriptions, or OCC: convex-performance-audit
    • Production migration, backfill, schema narrowing, or table reshaping: convex-migration-helper and create-and-cleanup-migration
    • Tables, TTL fields, cleanup crons, retention, auth/session cleanup, metric dedupe cleanup, or deprecated table removal: convex-retention
    • Auth, reusable components, or setup: use the matching managed Convex skill.

Do not edit upstream-managed Convex skills to store ClawHub policy.

Commands And Runtime Validation

  • Push new or changed functions before convex run:
    • dev: bunx convex dev --once
    • prod: deploy through the workflow described by clawhub-production-release
  • For a non-interactive direct production deploy when explicitly required, use bunx convex deploy -y.
  • If bunx convex run --env-file .env.local ... returns 401 MissingAccessToken after login, omit --env-file and target the deployment with --deployment <name> or --prod.
  • Regenerate committed API/types with bunx convex codegen after Convex API/schema changes.
  • Import mutations from convex/functions.ts, not convex/_generated/server, so ClawHub's trigger wrapper runs. Type imports can still come from convex/_generated/server.
  • Do not disable typechecking for an ordinary direct deploy. The production workflow owns its explicit deploy behavior and exceptions.

Mocked ctx tests cover pure business logic only. When behavior depends on pagination, indexes, validators, auth identity, internal/public boundaries, schedulers, actions calling functions, HTTP actions, storage, or OCC, also run a real Convex path such as:

  • bunx convex dev --once
  • bunx convex run ...
  • an HTTP action smoke
  • bun run test:pw:local-auth

Tests that invoke a mutation through ._handler need a mock database with normalizeId: vi.fn() for trigger-wrapper compatibility.

ClawHub Migration Boundaries

  • Default production data changes to @convex-dev/migrations; the companion skills own batching, dry runs, resume/progress, confirmation, validation, and cleanup.
  • Put component-backed table-wide backfills in convex/migrations.ts.
  • Put custom repairs, admin-gated operations, and incident-specific workflows in convex/maintenance.ts.
  • Keep one-off operator migration runs out of .github/workflows/deploy.yml.
  • Remove temporary migration functions in a follow-up PR after production apply and verification unless they are intentionally retained as maintenance tooling.

Query And Bandwidth Work

Use convex-performance-audit for the detailed rules on indexes, bounded reads, denormalization, digest tables, subscriptions, and function limits. Before writing or reviewing a performance-sensitive query, check deployment health when available:

bash
bunx convex insights --details
bunx convex logs --failure

Prefer measured runtime signals over speculative restructuring.

ClawHub Hot-Path Conventions

Use convex-performance-audit for the complete workflow. Preserve these ClawHub-specific implementations when touching their paths:

  • Public listing and browse pages use one-shot ConvexHttpClient.query() reads unless the user needs live updates.
  • When a skillSearchDigest row exists, resolve owner data with digestToOwnerInfo(digest). Do not re-read the owner document when the digest already contains the required owner fields.
  • Keep denormalized tables synchronized through the existing convex-helpers triggers and skip writes when derived fields did not change.
  • Paginate computed search results client-side after running the scoring pipeline once; do not rerun the full vector, lexical, and popularity pipeline for each page.
  • Add delayMs between backfill batches that update reactively subscribed tables.
  • Split mutations that would read more than the transaction budget through the existing action-query-mutation pattern.

Skill Stat Contract

The skills table still has a compatibility shape for four migrated stats:

Legacy nested fieldTop-level source of truth
stats.downloadsstatsDownloads
stats.starsstatsStars
stats.installsCurrentstatsInstallsCurrent
stats.installsAllTimestatsInstallsAllTime
  • Read these fields with readCanonicalStat() from convex/lib/skillStats.ts. It prefers the top-level field and falls back for pre-migration documents.
  • Write deltas with applySkillStatDeltas(). It updates both shapes in one patch.
  • Any direct patch touching these values must update both shapes.
  • Nested-only reads remain valid for stats.comments and stats.versions.
  • When adding a migrated stat field, use the same dual-write shape and add a cursor-based backfill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Clawhub Convex AI skill do?

Apply ClawHub-specific Convex conventions and route to the right managed Convex skill. Use for any change under convex/, Convex commands or deployment targeting, query performance, migrations, retention, runtime validation, or skill stat reads and writes in the ClawHub repository.

Why use Clawhub Convex on TypingMind?

Because you install it once and use it with any model. Clawhub Convex is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Clawhub Convex in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/clawhub-convex. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Clawhub Convex?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Clawhub Convex?

As many as you like. As long as a model supports skills, you can use Clawhub Convex with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Clawhub Convex AI skill free?

Yes. It is published on GitHub by openclaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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